The electronic device for diagnosing dementia with Lewy bodies (DLB) or predicting morbidity to DLB according to the present invention includes a processor that measures cortical thicknesses for a plurality of regions of the brain by using brain MRI images of a normal group and a DLB patient group, generates a DLB pattern matrix by using a residual matrix according to a difference between the average cortical thickness and the cortical thickness for each region, applies a first cortical thickness matrix generated by using a brain MRI image of the subject to the DLB pattern to calculate a first DLB pattern score, and diagnoses the subject as DLB or predicting morbidity to DLB by using the first DLB pattern score.

In embodiments, devices, methods and systems to analyze the different mediums of brain function in a mathematically uniform manner may be provided. These devices, methods and systems may manifest at several levels and ways relating to brain physiology, including neuronal activity, molecular chirality and frequency oscillations. For example, in an embodiment, a computer-implemented method for determining structure of living neural tissue may comprise receiving at least one signal from at least one read modality, the signal representing at least one physical condition of the living neural tissue, determining action potentials based on the signals received from the read modalities, determining frequency oscillations based on the signals received from the read modalities and the action potentials, and determining neuron network structures based on the signals received from the read modalities, the action potentials, and the frequency oscillations.

A method for determining a piece of data representing a cerebral marker. The piece of data is obtained from at least one brain network involved in performance of a given task. The is implemented by an electronic device and includes: obtaining data on encephalographic activities; processing the data on encephalographic activities, delivering at least one functional connectivity matrix representing connectivity between cortical sources derived from the data on encephalographic activities, each coefficient of the matrix representing connectivity between two cortical sources; statistical analysis of the at least one functional connectivity matrix delivering a probabilistic matrix of presence of at least one brain network; characterizing the at least one brain network on the basis of the at least one functional connectivity matrix and of the statistical analysis, delivering at least one brain network matrix; and obtaining a cerebral marker as a function of the at least one brain network matrix.

Embodiments are disclosed for passive tracking of dyskinesia and tremor symptoms using a wearable computer. In an embodiment, a method comprises: obtaining, by one or more motion sensors of a computer attached to a user's limb, motion data; extracting, by one or more processors of the computer, one or more features from the motion data that are potentially indicative of dyskinesia or tremor; determining, by one or more processors of the computer and based on the one or more extracted features, the likelihood of dyskinesia or tremor; generating, by the one or more processors, data indicating the likelihood of dyskinesia or tremor; and outputting, by the one or more processors, the data through an output device of the computer.

Disclosed are an apparatus and a method for gait type classification using a pressure sensor of a smart insole, which are capable of classifying pieces of gait data having various variances using only a pressure sensor. The apparatus for gait type classification includes a gait data measuring part configured to measure pieces of gait data using a pressure sensor, a pre-processor configured to define a section of a unit step in all the pieces of gait data, divide the pieces of gait data for each unit step, and normalize the pieces of divided gait data to equalize lengths of the pieces of divided gait data, and a feature extractor configured to extract features suitable for gait type classification from the pieces of pre-processed gait data, and a gait type classifier configured to receive the extracted features as an input and determine and classify a final gait type.

Embodiments regard nutrition assessment using a handheld device. An embodiment of an apparatus includes a handle with a controller within the handle, an attachment arm extending from the handle, and a user-assistive device coupled with an end of the attachment arm, wherein the apparatus is to determine a mass held by the user-assistive device, the determination being made during a task by a user of the handheld tool including manipulation of the handheld tool.

Markerless tracking systems and methods for markerless tracking of subjects. In one embodiment, the markerless tracking system includes an active 3D infrared camera, a memory, and an electronic processor. The electronic processor is configured to extract body motion data for a subject's body from depth data captured by the active 3D infrared camera. The electronic processor is also configured to detect movements of the subject's body using the body motion data. The electronic processor is further configured to determine attributes for the movements of the subject's body using the body motion data. The electronic processor is also configured to assign a rating by comparing the determined attributes with a plurality of benchmarks included in a pre-stored movement profile in the memory. The electronic processor is further configured to create a session record for the subject. The session record includes the body motion data, the determined attributes, and the assigned rating.

The present invention relates to methods for tuning treatment parameters in movement disorder therapy systems. The present invention further relates to a system for screening patients to determine viability as candidates for certain therapy modalities, such as deep brain stimulation (DBS). The present invention still further provides methods of quantifying movement disorders for the treatment of patients who exhibit symptoms of such movement disorders including, but not limited to, Parkinson's disease and Parkinsonism, Dystonia, Chorea, and Huntington's disease, Ataxia, Tremor and Essential Tremor, Tourette syndrome, stroke, and the like. The present invention yet further relates to methods of tuning a therapy device using objective quantified movement disorder symptom data acquired by a movement disorder diagnostic device to determine the therapy setting or parameters to be provided to the subject via his or her therapy device. The present invention also provides treatment and tuning remotely, allowing for home monitoring of subjects.

This disclosure relates to a system and method to implement a performance test to help evaluate a patient's neurological and cognitive function. The performance test can be executed by the patient autonomously using a portable computing device, such as a tablet computer or smart phone. The portable computing device can be programmed to execute a set of modules configured to assess motor and cognitive performance, such as a manual function test module, a cognitive processing speed test module, and a movement assessment test module. The set of modules can also include a collection module to aggregate test data from the manual function test module, the cognitive processing speed test module, and the movement assessment test module.

Restless Leg Syndrome (RLS) or Periodic Limb Movement Disorder (PLMD) can be treated using high frequency (HF) electrostimulation. This can include selecting or receiving a subject presenting with RLS or PLMD. At least one electrostimulation electrode can be located at a location associated with at least one of, or at least one branch of, a sural nerve, a peroneal nerve, or a femoral nerve. HF electrostimulation can be delivered to the subject, which can include delivering subsensory, subthreshold, AC electrostimulation at a frequency that exceeds 500 Hz and is less than 15,000 Hz to the location to help reduce or alleviate the one or more symptoms associated with RLS or PLMD. A charge-balanced controlled-current HF electrostimulation waveform can be used.